Nonparametric Estimates of Demand in the California Health Insurance Exchange

This paper considers uniformly valid (over a class of data generating processes) inference for linear functionals of partially identified parameters in cases where the identified set is defined by linear (in the parameter) moment inequalities. We propose a bootstrap procedure for constructing uniformly valid confidence sets for a linear functional of a partially identified parameter. The proposed method amounts to bootstrapping the value functions of a linear optimization problem, and subsumes subvector inference as a special case. In other words, this paper shows the conditions under which "naively" bootstrapping a linear program can be used to construct a confidence set with uniform correct coverage for a partially identified linear functional. Unlike other proposed subvector inference procedures, our procedure does not require the researcher to repeatedly invert a hypothesis test, and is extremely computationally efficient. In addition to the new procedure, the paper also discusses connections between the literature on optimization and the literature on subvector inference in partially identified models.

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Sharp Bounds on Treatment Effects for Policy Evaluation

Han¹,

Yang²

2020

Preprint

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For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to the policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This paper investigates the possibility of extrapolating local treatment effects to different counterfactual settings when instrumental variables are only binary. We propose a novel framework to systematically calculate sharp nonparametric bounds on various policy-relevant treatment parameters that are defined as weighted averages of the marginal treatment effect (MTE). Our framework is flexible enough to incorporate a large menu of identifying assumptions beyond the shape restrictions on the MTE that have been considered in prior studies. We apply our method to understand the effects of medical insurance policies on the use of medical services.

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Nonparametric Estimates of Demand in the California Health Insurance Exchange

Abstract: and participants in many conferences and seminars for helpful comments and feedback. Five anonymous referees and the co-editor provided comments that led to substantial improvements in the paper.

Cited by 3 publications

References 114 publications

Microeconometrics with Partial Identiﬁcation

Microeconometrics with Partial Identiﬁcation

Simple Inference on Functionals of Set-Identified Parameters Defined by Linear Moments

Sharp Bounds on Treatment Effects for Policy Evaluation

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